Quality Control Dissolution Data Is Biopredictive for a Modified Release Ropinirole Formulation: Virtual Experiment with the Use of Re-Developed and Verified PBPK Model

Physiologically based pharmacokinetic and absorption modeling are being used by industry and regulatory bodies to address various scientifically challenging questions. While there is high confidence in the prediction of exposure for the BCS class I drugs administered as immediate-release formulations, in the case of prolonged-release formulations, special attention should be given to the input dissolution data. Our goal was to develop and verify a PBPK model for a BCS class I compound, ropinirole, and check the biopredictiveness of the dissolution data for the prolonged-release formulation administered by Parkinson’s patients. The model was built based on quality control dissolution data reported in the certificates of analysis and verified with the use of data derived from five clinical trial reports. The simulated pharmacokinetic parameters being within a two-fold range of the observed values confirmed acceptable model performance, in vivo relevance of the in vitro dissolution profiles, and indirectly indicated ropinirole stable release from the formulation in the patients’ gastro-intestinal tract. Ropinirole PBPK model will be used for exploring potential clinical scenarios while developing a new formulation.

Introduction
Parkinson's disease is a progressive neurodegenerative disorder leading to motor and nonmotor disability [1,2]. There are a number of effective therapeutic options intended for the management of symptoms; however, none of them can stop the disease's progression [2,3]. Therefore, antiparkinsonian therapy is chronic and aims at achieving stable drug plasma concentrations without rapid fluctuations, in what further results in a stable therapeutic response [4,5]. To increase the level of patients' compliance and keep the plasma concentration in the blood stable, the prolonged-release formulations of antiparkinsonian drugs are often developed. One example is ropinirole, a dopaminergic drug that is used in the treatment of Parkinson's disease (PARKD) and restless leg syndrome (RLS) [6,7]. As the drug stimulates dopamine receptors, its continuous and smooth release from a formulation has crucial importance in terms of both efficacy and safety [8]. After the immediate-release tablets were first approved by the FDA in 1998, the 24-h prolonged-release formulation was developed by GSK and approved in 2008 [9]. Considering the fact that ropinirole is a valuable therapeutic agent in Parkinson's disease, the development of new generic formulations should be expected. The new formulations are usually selected based on the biopharmaceutical or clinical behavior anticipated in healthy adults and are subsequently used in patients without consideration of the physiological differences between populations [10]. In the case of Parkinson's disease, patient physiological parameters that could affect drug exposure can be divided into two categories: age-related and diseaserelated. Regarding the latter, Parkinson's disease is considered to be a systemic disease that changes gastro-intestinal (GI) conditions, which are crucial for drug absorption. However, these changes are often described as symptoms rather than as parameters, which makes it challenging to characterize them quantitatively [10]. Such an attempt was undertaken by Wollmer and Klein, who reviewed patient-specific GI parameters with the aim to create a platform for in vitro models for predicting in vivo performance of oral formulations administered to Parkinson's patients [11]. The authors concluded that while there is a considerable amount of information regarding salivary secretion, gastric emptying, and oropharyngeal and esophageal passage, more knowledge is required in the area of motility patterns and pressures in the GI tract, as well as GI fluid composition of Parkinson's patients. Such a knowledge gap makes it difficult to design a biorelevant dissolution method for antiparkinsonian drugs.
Taking into account the potential for new formulation development and the fact that ropinirole's PK might be affected by both age-and disease-related physiological parameters, it is a good practice to have a PBPK model which can help explore a number of different clinical scenarios and understand whether the quality control dissolution data can be biopredictive for an in vivo dissolution. The purpose of this study was to develop a PBPK model for ropinirole prolonged-release formulation in order to test the hypothesis of biopredictiveness of dissolution data obtained via the USP method without consideration of changes in GI tract of Parkinson's patients.

PBPK Model Development
All the modeling and simulation activities were conducted using Simcyp ® Simulator (V20, Certara, Sheffield, UK). Data for model development and verification was extracted from publicly available sources. Ropinirole mean concentration-time profiles were digitized using GetData Graph Digitizer [12], where necessary.
The PBPK model parameters are provided in Table 1 below.

Description of Distribution and Elimination
To reduce the potential interference of the absorption-related confounding factors and gain confidence in the ropinirole volume of distribution and systemic clearance, a PBPK model was initially developed for the immediate-release oral formulation [13]. The abovementioned parameters, namely clearance and volume of distribution, were estimated using a bottom-up approach due to the fact that intravenous clinical data for this compound is scarce [25]. To describe ropinirole distribution, a full PBPK model was chosen based on the compound's lipophilicity (logP = 2.7 [14]), the volume of distribution/bioavailability reported in the literature being around 7 L/kg, and the fact that the target for the drug action is located in a peripheral organ, namely CNS [26]. To incorporate clearance into the model, two approaches were considered: interspecies scaling and in vitro-in vivo extrapolation (IVIVE) [13]. The IVIVE approach has been chosen for the final model because systemic clearance predicted by the interspecies scaling did not properly describe multiple dosing scenarios. For illustration of the differences between two approaches, we simulated study No. 112771. It is worth noting that a fraction of the drug unbound in the incubation media (fu inc ) was not specified in the in vitro study report and was fitted in Simcyp using Nelder-Mead minimization method, 0.3 (0.00; 1.00) as initial value (lower bound; upper bound, respectively), maximum number of iterations 100, and weighted least-squares objective function.

Description of the Absorption
After defining the volume of distribution and systemic clearance, the next step was to model ropinirole absorption from a 24-h prolonged-release formulation. For this purpose, the advanced dissolution, absorption, and metabolism (ADAM) model implemented in Simcyp Simulator was chosen. ADAM model consists of nine anatomically relevant compartments: stomach, seven compartments reflecting the small intestine, and the colon. The drug's movement from one compartment to another is described as the first-order process. Additionally, the model allows accounting for intestinal efflux and metabolism. All the processes occurring in the GIT are described by the set of ordinary differential equations. The general form of the equations applicable for the small intestine is provided below [27]: Amount of solid mass available for dissolution (A S ): dA S,n dt = − dA diss,n dt − k t,n A S,n + k t,n−1 A S,n−1 + dA F,n dt (1) The amount of drug dissolved (A D ): dA D,n dt = dA diss,n dt − k deg,n + ka n + k t,n A D,n + k t,n−1 A D,n−1 + γ n CLu int−T,n f u gut C ent,n The drug concentration in the enterocytes (C ent ): where: n is the small intestine compartment, n = 1, 2, . . . , 7 dA diss,n dt : Dissolution rate k t,n : Transit time in the n-th segment of the small intestine A F,n : Amount of solid mass trapped in the formulation and not available for dissolution immediately k deg,n : Drug degradation rate constant (in lumen) ka n : Absorption rate constant γ n : The unit adjustment factor for the drug transported out of the enterocyte CLu int−T,n : Efflux clearance from the enterocyte CLu int−G,n : Metabolic clearance within the enterocyte In addition, ADAM model included Segregated Transit Time sub-model, which was used to define mean residence time for the fluid and dissolved drug in the stomach, small intestine, and colon.

Prolonged-Release Formulation
The prolonged-release formulation modeled in this study was Requip XL [9]. The tablet is based on Geomatrix technology [28] and consists of a slow-releasing active core sandwiched between two semi-permeable inactive layers. The list of typical excipients can be found in Requip XL Approval Package [9]. The complete release of the active substance from the formulation is achieved in 24 h.

Dissolution Data
All the dissolution-related processes were implemented into the model directly using quality control dissolution profiles extracted from GSK clinical study reports available at [29]. The details of the dissolution method were not disclosed in the published documents; however, it was stated that the dissolution profiles complied with USP <724> monograph requirements, allowing for the assumption of compendial conditions.
In addition, in a clinical pharmacology and biopharmaceutics review for Requip XL conducted by Center for Drug Evaluation and Research (hereinafter, CDER review) [9], the following dissolution method proposed by a Sponsor is described: USP Apparatus 2 with paddles, medium: citrate buffer at pH 4.0, paddle speed: 100 rpm. It is also stated that Requip XL dissolution was independent of the strength or the dissolution media (media with physiologically relevant pHs were tested), except for 0.1 M hydrochloric acid, where the drug release was slightly faster because of slightly impaired gelling of hypromellose in the acidic media.
It is important to highlight that the dissolution profiles, trial design, and concentrationtime profiles for the model verification were coming from the same source. The dissolution profiles reported in the certificates of analysis included three timepoints which were used to fit Weibull function parameters α and β: assuming F max = 100% and lag time = 0. Alpha and beta parameters of the Weibull fit for each formulation are presented in Table 1.
The representative dissolution profiles together with the Weibull fits are provided in Figure 1 below.

Prolonged-Release Formulation
The prolonged-release formulation modeled in this study was Requip XL [9]. The tablet is based on Geomatrix technology [28] and consists of a slow-releasing active core sandwiched between two semi-permeable inactive layers. The list of typical excipients can be found in Requip XL Approval Package [9]. The complete release of the active substance from the formulation is achieved in 24 h.

Dissolution Data
All the dissolution-related processes were implemented into the model directly using quality control dissolution profiles extracted from GSK clinical study reports available at [29]. The details of the dissolution method were not disclosed in the published documents; however, it was stated that the dissolution profiles complied with USP <724> monograph requirements, allowing for the assumption of compendial conditions.
In addition, in a clinical pharmacology and biopharmaceutics review for Requip XL conducted by Center for Drug Evaluation and Research (hereinafter, CDER review) [9], the following dissolution method proposed by a Sponsor is described: USP Apparatus 2 with paddles, medium: citrate buffer at pH 4.0, paddle speed: 100 rpm. It is also stated that Requip XL dissolution was independent of the strength or the dissolution media (media with physiologically relevant pHs were tested), except for 0.1 M hydrochloric acid, where the drug release was slightly faster because of slightly impaired gelling of hypromellose in the acidic media.
It is important to highlight that the dissolution profiles, trial design, and concentration-time profiles for the model verification were coming from the same source. The dissolution profiles reported in the certificates of analysis included three timepoints which were used to fit Weibull function parameters α and β: assuming Fmax = 100% and lag time = 0. Alpha and beta parameters of the Weibull fit for each formulation are presented in Table 1.
The representative dissolution profiles together with the Weibull fits are provided in Figure 1 below.   Table 2), while obs indicates extracted dissolution data and pred -Weibull fit. Additionally, Study No. ROP109087 for 8 mg was simulated with the use of dissolution data extracted from CDER review: the profiles in citrate buffer of pH 4.0 and in phosphate buffer of pH 6.8, which included eight timepoints each. The results were compared with those obtained with the three-timepoint dissolution profile from the certificate of analysis.

Intestinal Permeability
It was assumed that the drug intestinal transport is passive, therefore the result of PAMPA study was used as the model input (apparent permeability (Papp) = 26.8 × 10 −6 cm/s) [17]. The assumption is supported by a lack of reports providing information on ropinirole affinity to transporters [30]. Papp was scaled to the permeability in Caco-2 cell line based on the established correlation described in [30] and subsequently to human effective permeability (Peff) according to [31].

Fraction of Drug Unbound in the Enterocytes (fu gut )
Fraction of drug unbound, fu gut , was predicted by the Simcyp built-in model using the Rodgers and Rowland method (Method 2 on-screen in Simcyp Simulator) [32].

Intestinal Metabolism
Ropinirole is a soluble and permeable drug; therefore, intestinal metabolism is not of clinical relevance for this compound. Moreover, the main ropinirole metabolizing enzyme CYP1A2 is not expressed in the intestinal wall [23].
To summarize the model-building step, the assumptions of the ADAM model are provided below: • The release from the formulation is the only rate-limiting factor in the ropinirole's absorption.

•
In vitro dissolution data, obtained by the quality control method, is biopredictive as for ropinirole's behavior in the human GIT.

•
There is no drug degradation in the intestinal lumen.

•
The intestinal absorption is based on passive diffusion, and there are no efflux processes taking place at the intestinal wall. • Ropinirole intestinal metabolism is negligible.

•
The formulation provides stable active substance release in the Parkinson's patients GIT environment.

Model Verification and Application
Clinical study results from the reports mentioned previously were used for model verification. During the simulation exercises, the trial design was followed exactly as in the clinical trials. Namely, number of patients, age ranges, and male-to-female ratios in virtual trials were consistent with those in clinical trials. Demographic, anatomical, and physiological parameters of virtual patients were generated by the software using Monte Carlo approach based on the population libraries available in the Simcyp. For simulation of trials in healthy volunteers, Sim-Healthy Volunteers library was used. This virtual population is based on real life data from subjects who participated in Phase I studies [33]. For the simulation of trials in Parkinson's patients, Sim-NEurCaucasian population was used, considering the unavailability of the disease population. North European Caucasian population in Simcyp represents "general" rather than healthy population because it is difficult to define the latter, taking into account age-related changes in physiology together with minor and major health issues. The parameters of this population have been described previously [34]. The Geriatric population available in the simulator could not be used for simulation of the specified trials because the lower age limit of patients in the clinical trials was 34 years. The summary for trial design is provided in Table 2 below, including the indication of actual clinical trial populations versus virtual populations used in Simcyp.
It should be noted that healthy volunteers and patients in some cases administered ropinirole with domperidone. This combination does not bring any modification to the pharmacokinetics of either of them [35]. For the assessment of model performance, the simulated versus observed fold differences were calculated for Cmax, Tmax, and AUC (0-∞ or 0-24 depending on the study), along with the visual check of the concentration-time profiles. After the model verification step, we simulated the study ROP109087 in order to compare the drug exposure between the currently used group of patients (Sim-NEurCaucasian, aged 47-81) and three groups of geriatric population aged 65-75, 75-85, and 85-98 years old. For these simulation runs, we used Sim-Geriatric NEC population, which is based on Sim-NEurCaucasian population and takes into account age-dependent changes in physiological parameters [36].

Results
The model was verified by simulating five clinical trials and covered all the strengths available on the market (from 2 to 12 mg), as well as fasted and fed states. The simulated versus observed fold values for Tmax, Cmax, and AUC are provided graphically in Figure 2.
All the values were within a two-fold range, which was the criteria for PBPK model performance assessment used in this study. The only exception was Tmax fold in study No. 101468-219 (1 mg), which was 2.37. For calculation of fold differences, mean, geometric mean, or median values were extracted from the studies (depending on availability in the report), and the respective values were generated by the simulator, both from the statistical perspective (for example, mean observed vs. mean simulated) and from the PK perspective (for example, AUC0-inf observed vs. AUC0-inf simulated).
including the indication of actual clinical trial populations versus virtual populations used in Simcyp.
It should be noted that healthy volunteers and patients in some cases administered ropinirole with domperidone. This combination does not bring any modification to the pharmacokinetics of either of them [35].
For the assessment of model performance, the simulated versus observed fold differences were calculated for Cmax, Tmax, and AUC (0-∞ or 0-24 depending on the study), along with the visual check of the concentration-time profiles. After the model verification step, we simulated the study ROP109087 in order to compare the drug exposure between the currently used group of patients (Sim-NEurCaucasian, aged 47-81) and three groups of geriatric population aged 65-75, 75-85, and 85-98 years old. For these simulation runs, we used Sim-Geriatric NEC population, which is based on Sim-NEurCaucasian population and takes into account age-dependent changes in physiological parameters [36].

Results
The model was verified by simulating five clinical trials and covered all the strengths available on the market (from 2 to 12 mg), as well as fasted and fed states. The simulated versus observed fold values for Tmax, Cmax, and AUC are provided graphically in Figure  2. All the values were within a two-fold range, which was the criteria for PBPK model performance assessment used in this study. The only exception was Tmax fold in study No. 101468-219 (1 mg), which was 2.37. For calculation of fold differences, mean, geometric mean, or median values were extracted from the studies (depending on availability in the report), and the respective values were generated by the simulator, both from the statistical perspective (for example, mean observed vs. mean simulated) and from the PK perspective (for example, AUC0-inf observed vs. AUC0-inf simulated).  Table 2. Latin letters indicate Simulation ID referenced in Table 2.
The visual checks are provided in Figure 3. Figure 3a-e represents single-dose studies whereas Figure 3f-n represents multiple-dose studies. The multiple-dose simulations are additionally zoomed in the timeframe where the observed data were available. Figure 4 illustrates the model performance when IV clearance was predicted based on interspecies scaling and used in the model as a whole value. The simulation results with the use of  Table 2. Latin letters indicate Simulation ID referenced in Table 2. The visual checks are provided in Figure 3. Figure 3a-e represents single-dose studies whereas Figure 3f-n represents multiple-dose studies. The multiple-dose simulations are additionally zoomed in the timeframe where the observed data were available. Figure 4 illustrates the model performance when IV clearance was predicted based on interspecies scaling and used in the model as a whole value. The simulation results with the use of different dissolution profiles are provided in Figure 5. Results of trial simulations in geriatric population are presented in Figure 6.  Table 2. Latin letters indicate Simulation ID referenced in Table 2.
The visual checks are provided in Figure 3. Figure 3a-e represents single-dose studies whereas Figure 3f-n represents multiple-dose studies. The multiple-dose simulations are additionally zoomed in the timeframe where the observed data were available. Figure 4 illustrates the model performance when IV clearance was predicted based on interspecies scaling and used in the model as a whole value. The simulation results with the use of different dissolution profiles are provided in Figure 5. Results of trial simulations in geriatric population are presented in Figure 6.    Table 2. Each Latin letter in this figure is a reference to the Simulation ID in Table 2. Multiple dose simulations additionally zoomed (e.g., f-n zoomed) in the time period where the observed data was available.  Table 2. Each Latin letter in this figure is a reference to the Simulation ID in Table 2. Multiple dose simulations additionally zoomed (e.g., f-n zoomed) in the time period where the observed data was available.  Table 2. Each Latin letter in this figure is a reference to the Simulation ID in Table 2. Multiple dose simulations additionally zoomed (e.g., f-n zoomed) in the time period where the observed data was available.

Discussion
Physiologically based pharmacokinetic and absorption modeling are being used by industry and regulatory bodies to address various scientifically challenging questions. The PBPK models describing mechanistic absorption process of orally taken drugs have

Discussion
Physiologically based pharmacokinetic and absorption modeling are being used by industry and regulatory bodies to address various scientifically challenging questions. The PBPK models describing mechanistic absorption process of orally taken drugs have

Discussion
Physiologically based pharmacokinetic and absorption modeling are being used by industry and regulatory bodies to address various scientifically challenging questions. The PBPK models describing mechanistic absorption process of orally taken drugs have been less commonly reported as part of regulatory approvals in comparison to other application areas. According to Zhang et al., the distribution of PBPK submissions by application areas in 2018-2019 included predominantly the DDI-related PBPK analyses (56%) of the total number of applications, while absorption accounted for 7% [37]. For the current project, detailed, case-by case-analysis was applied, yet there is high confidence in the predictions for the BCS class I drugs [38,39]. The purpose of this study was to develop and verify a PBPK model for the BCS class 1 compound ropinirole, where the model is supposed to be able to predict ropinirole exposure of prolonged-release formulation for oral administration in various doses to the patient population.
The key PK parameters of the model were already verified in the number of simulations for the immediate-release formulation described recently in [13]. As ropinirole is mostly eliminated by enzymatic metabolism in the liver, special attention was given to the IVIVE method of the clearance prediction after the interspecies scaling approach occurred to not be sufficient for the prediction of most of the multiple-dose studies. Please refer to the predictions obtained with the use of in vivo clearance from interspecies scaling after IV administration in Figure 4. For the comparison with IVIVE approach, please see Figure 3a,k.
The simulations of the same studies with the use of the IVIVE approach are presented in Figure 3a (single-dose study) and Figure 3k (multiple-dose study). We hypothesized that the main reason that the IVIVE approach was more efficient lies in accounting for the inter-individual variability in the clearance-related physiological parameters. For this reason, for clearance prediction in the final PBPK model, an IVIVE approach was used.
The absorption of ropinirole from modified release formulation was modeled using a less mechanistic approach: the dissolution was inputted into the model directly using quality control data and effective human permeability was predicted using an empirical correlation equation. Dissolution data are the key input parameter for the PBPK model, and in cases where the dissolution data are used in the model "as is" rather than modeled mechanistically, the assumption about their relevance to the in vivo dissolution has to be made. For reaching the point where this assumption is confirmed/rejected, a strong confidence in distribution and elimination parameters is needed. Due to the lack of intravenous data for ropinirole, this aim was achieved by conducting the modeling and simulation (M&S) activities for the immediate-release formulation, where absorption was not a rate-limiting step. Only after successful simulation of number of single-dose and multiple-dose clinical studies in healthy volunteers and patient populations could we fix the distribution and elimination parameters and move to the following step: M&S for the prolonged-release formulation, where the release from the formulation was a rate-limiting step. The model was initially developed for healthy volunteers and was subsequently extrapolated to the Parkinson's patients. In all cases, quality control dissolution profiles were used as an input to the absorption model. While looking at simulation results for healthy volunteers and Parkinson's patients, it can be observed that the performance of the model is similar both in Parkinson's patients and healthy volunteers. Please see Figure 3a-e for the simulation of studies with healthy volunteers and Figure 3f-n for the simulation of studies in Parkinson's patients. The fact that the model, which was developed for healthy volunteers, could be extrapolated to the patient population without any modification of physiological parameters besides those that are age-related allows for the following conclusions: (1) the quality control dissolution data are biopredictive for the in vivo situation; (2) even if there are changes in the gastro-intestinal parameters of Parkinson's patients, these changes do not influence the release of the drug from the prolonged-release formulation, which is based on Geomatrix technology. We assumed no impact of fluid dynamics, pressures, or motility patterns in patients' GIT on release patterns of the formulation. This still allowed the model predictions to align with the PK profiles observed in patients, thus indirectly confirming that these parameters would not have a clinically significant effect on formulation behavior in vivo. In other words, it can be concluded that the formulation is stable in the Parkinson's patient GI environment.
Because the dissolution profiles used in the simulations composed of three timepoints only (2, 12, and 24 h), it was decided to conduct extra simulations using the dissolution data from CDER review. We compared three simulation scenarios: (1) with the use of a three-timepoint profile from the certificate of analysis (it was assumed that the profile was obtained at pH 4.0); (2) with the use of eight-timepoint profile from CDER review obtained at pH 4.0; (3) with the use of eight-timepoint profile from CDER review obtained at pH 6.8. The results of these simulations, which are provided in Figure 5, suggest that the three-timepoint profile is reflective of the profile with eight-timepoints. In addition, despite it being claimed that dissolution of the formulation is pH-independent, our simulation run on dissolution data obtained at different pH shows that the model is sensitive to these changes and that the profile obtained at pH 4.0 gives a more realistic prediction than the profile obtained at pH 6.8.
Parkinson's disease is rarely diagnosed at age under 40 and its incidence increases with age [1]. Older patients are generally underrepresented in the clinical trials; therefore, PBPK can be a useful alternative approach for predicting drug pharmacokinetics [37]. The simulation of trials in the three groups of geriatric population have indicated that drug exposure increases with age, which is expected based on the fact that ropinirole is mainly metabolized in the liver and metabolic rates are slower in older patients [26]. The increased drug exposure in older patients has to be taken into account during pharmacokinetic optimization of ropinirole's therapy [4].

Conclusions
In this study, ropinirole PBPK model for the prolonged-release formulation intended for exploring the potential clinical scenarios while developing a new formulation was built and verified. In vitro data obtained by the quality control method implemented into the ADAM model adequately described the in vivo behavior of the formulation both in healthy volunteers and in Parkinson's patients. The assumption about the stable release from the formulation in the GIT of the patient population was indirectly confirmed. It is worth noting that this PBPK model is formulation specific; however, we believe that it can be used for our research purposes after the mechanical stability (e.g., resistance to pressure) of the new formulation is established.
The performed simulations allowed us to additionally verify in vitro kinetic parameters for ropinirole metabolism, as well as its metabolic pathways. In this study, most of the datasets were multiple-dose studies, and new strengths, which are not available in the immediate-release case, were used.

Conflicts of Interest:
Sebastian Polak is an employee of Certara U.K. Limited, Simcyp division. Certara U.K. Limited had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.


extfieldsofstudy: Medicine Psychology

text:
Time Course of Recovery Following CrossFit® Karen Benchmark Workout in Trained Men

The establishment of fatigue following the acute exercise stimulus is a complex and multi-factorial process, that might arise due to a range of distinct physiological mechanisms. However, a practical method of assessing CrossFit® athletes’ recovery status has been neglected entirely in real-world sporting practice. The study describes the acute and delayed time course of recovery following the CrossFit® Benchmark Workout Karen. Eight trained men (28.4 ± 6.4 years; 1RM back squat 139.1 ± 26.0 kg) undertook the Karen protocol. The protocol consists of 150 Wall Balls (9 kg), aiming to hit a target 3 m high. Countermovement jump height (CMJ), creatine kinase (CK), and perceived recovery status scale (PRS) (general, lower and upper limbs) were assessed pre, post-0h, 24, 48 and 72 h after the session. The creatine kinase concentration 24 h after was higher than pre-exercise (338.4 U/L vs. 143.3 U/L; p = 0.040). At 48h and 72 h following exercise, CK concentration had returned to baseline levels (p > 0.05). The general, lower and upper limbs PRS scores were lower in the 24-h post-exercise compared to pre-exercise (general PRS: 4.7 ± 1.5 and 7.7 ± 1.7; p = 0.013; upper limbs PRS: 6.6 ± 1.3 and 7.5 ± 1.3; p = 0.037; lower limbs PRS: 3.9 ± 2.5 and 7.3 ± 0.1; p = 0.046). Our findings provide insights into the fatigue profile and recovery in acute CrossFit® and can be useful to coaches and practitioners when planning training programs. Moreover, recovery status can be useful to optimize training monitoring and to minimize the potential detrimental effects associated with the performance of repeated high-intensity sessions of CrossFit®.

INTRODUCTION
CrossFit ® training programs are usually characterized by a high training intensity, with most of the sessions being performed at high intensities (Meyer et al., 2017). The training sessions contemplate the development of multiple physical abilities, through the use of different exercises such as weightlifting exercises (clean and jerk, snatch, and its variations), powerlifting (bench press, overhead press, deadlift, front, and back squat), and metabolic conditioning (Claudino et al., lactate up to 18 mmol/L), and to high levels of fatigue, impairing the ability to repeat the initial performance in a countermovement jump, a potential indicator of neuromuscular fatigue, are also seen immediately after the sessions. These effects may last up to 48 h, depending on the characteristics of the session performed (Claudino et al., 2017;Cooper et al., 2020). In addition, the high number of repetitions performed, often to the point of muscular failure, increase markers of exercise-induced muscle damage (interleukin-6 -IL-6, and creatine kinase-CK), with these concentrations remaining elevated up to 24 h post-exercise (Claudino et al., 2018).
When comparing the perceptual responses and post-exercise physical disfunction between a CrossFit ® session and a session based on the guidelines of the American College of Sports Medicine, Drum et al. (2017) found significant differences between sessions. CrossFit ® participants reported a higher rating of perceived exertion (RPE) and a greater perceived number of hard training days per week. Also, feelings of excessive fatigue, muscle soreness, muscle swelling, shortness of breath, muscle pain to light touch, and limited movement in muscles used during exercise within 48-h post-exercise were also higher in CrossFit ® participants. However, these responses were observed in a crosssectional study, which limits the understanding of the cause-effect relationship (Wang and Cheng, 2020) that exists between a specific CrossFit ® Workout session and physiological outcomes. Since adaptations caused by exercise training may result from the temporal summation of acute responses (Rockl et al., 2008), understanding the role of recovery status in a time-dependent manner is first to step to understand fatigue status.
Comprehending the time-course of recovery following CrossFit ® session is important for minimizing the risk of maladaptation due to insufficient recovery between each stimulus and might assist in ensuring optimal exercise monitoring. The development of fatigue following the individual's physiological and perceptual responses to a stimulus, is a complex and multi-faceted phenomenon, that might arise due to a variety of different mechanisms (Halson, 2014). Recovery, therefore, is also a multifactorial process, and as such, the assessment of the recovery-fatigue continuum should be relative to the demands of the sport or activity performed (Kellmann et al., 2018). While performance measures represent the most sport-specific outcomes, other physiological and psychological measures provide integral information on an athlete's recovery (Kellmann et al., 2018). Stress markers such as creatine kinase (CK) counter movement jump (CMJ) and perceived recovery status (PRS) remain largely unknown in CrossFit ® training programs, despite their potential to identify athletes' recovery status following exhaustive sessions .
Despite the importance of performance and physiological markers, an athlete's perception of their "readiness to perform" can also be described as a critical determinant of recovery. In this context, Laurent et al. (2011) proposed a "Perceived Recovery Status" (PRS) scale, which is similar but opposite to a perceived exertion scale (RPE) (10-12). Both scales are based on the psychophysiological status of the athlete. However, while the rating of perceived exertion (RPE) is utilized during or after a session, the PRS scale is utilized prior to the session to identify the athletes' recovery status. The PRS scale has been shown to be a reliable tool to assess the perceived recovery state of individuals, demonstrating accuracy (>80%) in identifying changes in performance when the individuals reported feelings of being under-recovered (Laurent et al., 2011). A practical method of assessing athletes' recovery status prior to a session might allow coaches and practitioners to adjust the training session to match the individuals' current recovery status, potentially optimizing training outcomes (Laurent et al., 2011;Sikorski et al., 2013).
Thus, the purpose of this study is to describe the acute and delayed time course of recovery following the CrossFit ® benchmark workout Karen in healthy trained subjects. The development of fatigue following the individual's physiological and perceptual responses to a stimulus, is a complex and multifaceted phenomenon, that might arise due to a variety of different mechanisms it was hypothesized that the PRS scale would provide an accurate assessment of the participants' recovery status, and that this would be mirrored by the changes in CK and muscle performance, assessed via a countermovement jump (CMJ). This variety of tools to monitor recovery are practical for daily use due to low cost and time accompanied by simple interpretations.

Participants
Eight male subjects (age 28.4 ± 6.4 years old; 1RM back squat: 139.1 ± 26.0 kg) were recruited. All participants were free of injury and known illnesses, were not using drugs to enhance performance, and had a minimum experience of 6 months with CrossFit ® and were familiar with all exercises used in the study.
The subjects trained five times a week, each training session consisting of approximately 10 min of warm-up, 40 min of strength and power training, and 20 min of metabolic conditioning. Indirect maximal aerobic capacity (VO 2 max), assessed via a maximal 2-km rowing test (Klusiewicz et al., 2016;Tibana et al., 2021) and strength (1RM) are described in Table 1, and were assessed 2 weeks before the participants completed the testing protocol. Participants performed one repetition maximum (1 RM) test for back squat according to procedures recommended by the National Strength and Conditioning Association (Lloyd et al., 2016). During this exercise period, standard instructions regarding the procedures of the test protocols and the appropriate execution of the exercise technique were supplied by an experienced investigator (Tibana et al., 2021). The participants were advised to refrain from ingesting alcohol in the 24 h before any of the tests, to avoid exercise in the 48 h before the protocol and in the 72 h after the workout of the day (WOD), and to maintain their normal daily diet and hydration during the study. All participants signed an informed consent document, and the study was approved by the University Research Ethics Committee for Human Use (2.698.225; 7 June 2018) and conformed to the Helsinki Declaration on the use of human participants for research.

Experimental Design
This study was designed to analyze the time-course of recovery of physiological, psychological and performance responses in trained adult men, following the completion of the CrossFit ® benchmark workout Karen. The protocol consists of 150 repetitions of wall balls, with athletes aiming to hit a target 3 m high, using a 9 kg medicine ball. All participants were experienced with the protocol, having previously performed it a minimum of 4 times as part of their own training. Each participant performed the session individually. In this study, the benchmark Karen was the independent variable, while the dependent variables consisted of changes in creatine kinase, countermovement jump and PRS scale (general, lower, and upper limbs) ( Figure 1).

Karen Protocol
The CrossFit ® WOD Karen corresponds to a timed protocol that utilizes one element (medicine ball throws; 9.07 kg for a height of 3 m). The aim is to complete the task of performing 150 medicine ball throws to a wall in the shortest time possible. Therefore, a better performance in this WOD is indicated by a shorter time to complete the protocol. The Karen protocol was chosen because it consists of only one exercise and because of the large number of repetitions performed as fast as possible. Also, Karen protocol is very popular and extremely usual among the WOD routines.

Creatine Kinase and Blood Lactate Analysis
Whole-blood creatine kinase activity was assessed from a single fingertip capillary sample with the subject in a seated position. After pre-warming the hand, a sample of blood (30 μL) was obtained and analyzed using a colorimetric assay procedure (Reflotron, Boehringer Mannheim, Germany). Before each testing session, quality control (calibration) measurements were undertaken according to the manufacturer's recommendations. The ''normal'' reference range for creatine kinase activity, as provided by the manufacturer, is 24-195 U/L. The blood lactate collection, management, and analysis were determined according to Falk Neto et al. (2020). Capillary blood samples were collected through a transcutaneous puncture on the medial side of the tip of the middle finger using a disposable hypodermic lancet (Falk Neto et al., 2020). Blood lactate concentration was determined by photometric reflectance on a validated Portable Accutrend Plus system (Roche, Sao Paulo, Brazil).

Perceived Recovery Scale
Immediately before the training sessions, the athletes were asked to rate their recovery status according to the PRS Scale. The scale ( Figure 2) ranges from 0 to 10, with a score of "0" indicating that the athlete is "very poorly recovered/extremely tired" and a score of "10" indicating that the athlete is "very well recovered/highly energetic". A score of 0, 1, or 2, is associated with an expected reduction in performance, while a score of 8, 9, or 10, means an improvement in performance is expected. The range of values between three and seven indicate that no changes in performance are expected (Laurent et al., 2011).

Countermovement Jump Height
For the CMJ height, a jump platform (Jump System 1.0, Cefise Ltda.) was used. The athlete was positioned, barefoot, in the interior of the platform, with their hands fixed at their waist. The test consisted of performing a maximal vertical jump. The athletes were instructed to swing their arms back and aim to jump as high as possible while using the momentum created with their movement. Two jumps were performed with a 1-min interval between them. The participants' highest jump (in centimeters) was considered as the maximal CMJ height and utilized for subsequent analyses (Haugen et al., 2020). The CMJ was chosen because is a simple, practical, valid, and very reliable measure of lower-body power. The CMJ has been shown to be the most reliable measure of lower-body power compared to other jump tests (Petrigna et al., 2019).

Statistical Analysis
The data are expressed as mean value ±standard deviation (SD Cohen's f effect size (ES) for ANOVA was calculated to estimates the proportion of variance in the present sample. The Cohen's f effect size is classified as: small (f = 0.10); medium (f = 0.25); large (f = 0.40). The power of the sample size (1-ß) was determined using post hoc analysis on G*Power version 3.1.9 (Faul et al., 2007) and it is presented in the results section for each analysis. The Pearson correlation was used to evaluate correlations between PRS, CK and CMJ (pre-test, 24, 48, and 72 h postsession values grouped). The magnitude of the correlations was classified as: r ≤ 0.1 trivial; 0.1 < r ≤ 0.3 small; 0.3 < r ≤ 0.5 moderate; 0.5 < r ≤ 0.7 large; 0.7 < r ≤ 0.9 very large; > 0.9 almost perfect (Hopkins et al., 2009). The level of significance was p ≤ 0.05 and SPSS version 20.0 (Somers, NY, United States) software was used.

Completion Time
The average time to complete the 150 repetitions of wall ball was 597 ± 111.6 s. The fastest volunteer completed the exercise session in 495.6 s and the slowest in 795 s.

Physiological, Biochemical, and Neuromuscular Responses
The blood lactate concentration and RPE presented a statistically significant increase after the exercise session (blood lactate concentration, pre: 3.0 ± 0.7 mmol/L and post: 17.5 ± 3.0 mmol/L, p ≤ 0.005; ES = 4.63; RPE, pre: 1.6 ± 0.5 and post: 9.0 ± 0.8 mmol/L, p ≤ 0.005; ES = 10.59). There was a statistically significant effect of time on CK, F (4, 24) = 8.31, p < 0.0005, ES = 0.58, observed power = 0.99. The CK concentration 24 h after the exercise session was statistically significant higher that pre-exercise concentration (p = 0.040; Figure 3). No statistically significant differences were observed between 0-(p = 0.241) 48-(p = 0.608) and 72-h (p = 0.973) after exercise and pre-exercise concentrations. The Karen protocol had a statistically significant effect on CMJ, F (4, 28) = 4.14, p = 0.046, ES = 0.37, observed power = 0.59. The height of CMJ postexercise was statistically significantly lower than pre-exercise (p = 0.043; Figure 4). However, no statistically significant differences were observed in the height of CMJ between pre-exercise and 24-(p = 0.108), 48-(p = 0.459) and 72-h (p = 0.827) post-exercise.  The comparison between the scores of general, lower, and upper limbs of PRS was presented in Figure 6. No statistically significant differences were observed between PRS scales pre-(p = 1.000 between general and upper PRS scores; p = 0.262 between general and upper PRS scores; p = 1.000 between lower and upper PRS scores) and 72 h post-exercise (p = 0.107 between general and upper PRS scores; p = 0.332 between general and upper PRS scores; p = 0.093 between lower and upper PRS scores). However, 24-and 48-h post-exercise, the PRS of upper limbs was statistically significantly higher than general PRS (p = 0.015 for 24-h and p = 0.030 for 48-h) and PRS of lower limbs (p = 0.041 for 24-h and p = 0.014 for 48-h). Finally, 48-h post-exercise, the PRS of lower limbs was statistically significantly lower than general PRS (p = 0.037). Table 2 shows the correlations between the PRS scales, CK concentration and height of the CMJ. It was observed only a statistically significant correlation between PRS of upper limbs and height of the CMJ (p < 0.0005; r = 0.533; large).

DISCUSSION
The aim of this study was to analyze the physiological, biochemical, and neuromuscular responses following a CrossFit ® benchmark session and to assess if the PRS scale could be a practical tool to determine the athletes' readiness to train status. The main findings partially confirm the initial hypothesis, revealing 1) significant increases in blood lactate post-exercise; 2) an increase of CK concentration 24 h postexercise, returning to baseline levels 48 h post-exercise; 3) a significant change in the participants' perceived recovery status PRS for upper and lower limbs 24 h post-exercise     Gomes et al., 2020;Martinez-Gomez et al., 2022). Importantly, this study highlights the potential of the PRS scale to be used as a marker of recovery status following a Crossfit ® session. CrossFit ® training sessions are often performed with nearmaximal or maximal efforts, leading to a significant metabolic stimulus (Tibana et al., 2016;Claudino et al., 2018). In this context, blood lactate concentration has been utilized as a reliable marker to assess the intensity of different sessions of CrossFit ® (Falk Neto et al., 2020). While changes in blood lactate concentrations will be dependent on the duration and intensity of the sessions performed (Özsu et al., 2018), previous research has shown that different CrossFit ® sessions incur high blood lactate levels (Toledo et al., 2021). Timon et al. (2019) analyzed the blood lactate responses of two different protocols (Protocol 1: AMRAP of Burpees and Toes to Bar increasing repetitions (1-1, 2-2, 3-3 . . . ) in 5 minutes; Protocol 2: three rounds of 20 repetitions of wall ball (9 kg) and 20 repetitions of power clean (40% 1RM) in the shortest possible time), with protocol two showing a similar lactate response as the one seen in this study (18.38 ± 2.02 mmol/L vs. 17.5 mmol/L ±3.0 mmol/L). Despite a similar perception of effort, it seems that protocols that do not use an external load (protocol 1) have a smaller lactate response (Timon et al., 2019). Still, the metabolic response in these sessions is considered high, even in the absence of an external load. For example, a session requiring participants to complete as many rounds as possible (AMRAP) of two exercises (burpees and toes to bar) still elicited a high blood lactate response (13.3 ± 1.87 mmol/L). Tibana et al. (2016), analyzing a session that involved AMRAP of double under and rowing, and Maté-Muñoz et al. (2017) with a session that consisted of performing a single exercise (double unders), also reported a high lactate response (9.05 ± 2.56 vs. 10.37 ± 2.91 mmol/L), respectively. In addition, even when the intensity of a CrossFit ® session was manipulated to be performed at a lower perception of effort (6 out of 10, utilizing the Borg CR-10 scale), the lactate responses were still quite high (12.8 ± 3.2 mmol/L) (Alsamir Tibana et al., 2019). Previous studies have demonstrated that the metabolic responses induced by a training session are related to the required time to recover from this stimulus (Özsu et al., 2018). Considering the high physiological stress induced by CrossFit ® sessions, even when there is no external load, or when the intensity is controlled, understanding the time-course of recovery from these sessions is essential to ensure athletes can optimize their training. The serum CK is often utilized to understand the recovery status of participants following a training session given its easy of collection and analysis (Halson, 2014). The CK concentrations can be raised due to exercise induced muscle damage as a consequence of intense and prolonged training. The peak of serum CK normally occurs about 12-24 h after a strength training session, and values can remain elevated for up to 96 h when the exercise is focused on the eccentric phase of the movement (Baird et al., 2012). Importantly, CK values have been associated with muscle injury (Hyatt and Clarkson, 1998;Halson, 2014). Studies involving CrossFit ® showed significant increases in CK that could be pathological due the extremely high values (Tibana et al., 2018b;Meyer et al., 2018). The present study found increases in CK 24 h post-exercise, with the values returning to baseline 48 h postexercise. These results are in agreement with Timon et al.  Tibana et al. (2019) showed that after five workouts over three consecutive days of competition the peak CK concentration occurred 24 h postexercise (~698.7 U/L). Thus, it seems that when the CrossFit ® session does not elicit increases in CK concentration that could be considered pathological, the concentrations might return to baseline levels within 48 h.   In addition to changes in CK concentrations, CMJ height alterations might also be utilized as a potential marker of fatigue (Claudino et al., 2017). A recent study analyzed CMJ height as a measure to assess neuromuscular status following a CrossFit ® competition . The CMJ jump height was significantly reduced 24-h post competition, with the values collected at 48-and 72-h post competition showing no differences from baseline. However, Tibana et al. (2016) demonstrated that consecutive days of CrossFit ® training, despite eliciting significant metabolic changes, did not lead to impairments in muscle power. Considering that CrossFit ® sessions vary often in the exercises performed and consequently, muscle groups utilized, and their duration, it is possible that CMJ height might have limited application as a measure to monitor the athletes' neuromuscular status, particularly after single bouts of exercise.
The novel finding of this study is that while objective measures (CK and CMJ height) indicate that the participants might be fully recovered from a session within 24-48 h, the psychobiological monitoring of the athlete's perceived recovery state indicates that 48-72 h might be needed for the athletes to return a point where performance is expected to be the similar or improved, based on the PRS. Psychobiological monitoring of training status is a noninvasive and non-exhaustive measure of assessing fitness (e. g. stress, fatigue), and also presents an effective and inexpensive measure to assess individual responses to training and competition. Despite its possibility as a tool to monitor current training status, Bishop et al. (2008) reported that there is still a limited knowledge by trainers and athletes about how to utilize such tools to optimize training intensity and recovery within a microcycle. Nevertheless, the large effect sizes reported indicate that further studies are required to assess the efficacy of the general PRS scale to determine the athletes' recovery status. The different time course of recovery for the upper and lower limbs, with the perception of recovery for the lower limbs taking a longer time to return to baseline levels, has practical significance in CrossFit ® . Coaches and practitioners can potentially use this information to prescribe the next training bout in a way that respects the recovery time required following the previous session. In this scenario, prescribing a training session that focuses on the upper or lower body, or controlling the intensity of the subsequent session might assist coaches in reducing the intensity of the subsequent session, when required, or to reduce the level of physiologic stress, consequently, properly managing the athlete's training load (Tibana et al., 2018a;Falk Neto et al., 2020). While these would be important outcomes to ensure improved training prescription in the modality, further studies are required in this topic.
Despite a range of instruments to monitor recovery have been established, many are impractical for daily use due to cost, time, and challenges with interpretation (Lee et al., 2017;Seshadri et al., 2019). The results in this study demonstrate that a practical, noninvasive and expeditious approach to monitoring the participant's recovery following an acute CrossFit ® session might provide important information for coaches and practitioners. In particular, the time-course of recovery according to the PRS is similar to that of the CK responses, with both measures reaching its most extreme values 24 h after the training session. However, while CK responses recover faster in the subsequent 24 h, the athletes' perceived recovery might show a slower improvement, particularly for the lower limbs based on the protocol used in this study. Therefore, this study demonstrates that the PRS may be useful in allowing appropriate adjustments in training intensity or volume in CrossFit ® based on the athletes' recovery status. Considering the potentially detrimental effects of performing numerous maximal or nearmaximal CrossFit ® sessions in a short period of time, the use of the subsets of the PRS scale (upper and lower limbs) might assist in optimizing training prescription, providing important information about when the next stimulus should be provided, according to the athletes recovery status. Future studies should investigate if the use of the PRS scale might, in fact, optimize training prescription while helping to reduce the incidence of muscle injuries and the onset of non-functional overreaching.
Some limitations of the present study must be emphasized. Particularly, the reduced numbers of participants, the lack of control over the participants' diet prior to the test must be acknowledged. In addition, other factors that could influence the participants' recovery such as sleep, and stress have not been assessed during this study. Caution is advised when extrapolating the results of the current study to other populations or individuals of different training experience, as only healthy, experienced and male participants were recruited in this study. Our findings should not be generalized for other WOD and exercises. Moreover, our results cannot be used to infer the effects of combining these sessions within a larger training week, including a match stimulus and other modes of training (i.e., gymnastics, strength, power, and cardiorespiratory training). Future studies of a similar nature should include other critical biomarkers and an upper limb power measures to elucidate the time course of recovery and whether a state of fatigue truly occurred. Further longitudinal studies analyzing fatigue status and recovery in response to CrossFit ® training over several days using similar methods can be relevant to further our understanding of the performance changes, and fatigue and recovery markers in different subjects.

CONCLUSION
In summary, a single CrossFit ® session using repeated wall-ball movements elicited a significant level of metabolic stress, along with an increase in CK levels in the 24-h after the exercise session. More importantly, the results showed the potential utility of the PRS scale as noninvasive tool for accurately monitoring recovery status in CrossFit ® practitioners. Particularly, the subscales of the PRS (upper and lower limb) seemed to be more effective at assessing changes in the athletes' perceptions of recovery following an acute session. Coaches, sport scientists, and practitioners could implement the use of these scales PRS to obtain important insights into the recovery status of the participants. While this information can be useful to coaches to optimize training monitoring and to minimize the potential


s2fieldsofstudy: Biology Chemistry

text:
The BASP1 transcriptional corepressor modifies chromatin through lipid-dependent and lipid-independent mechanisms

Summary The transcriptional corepressor BASP1 requires N-terminal myristoylation for its activity and functions through interactions with nuclear lipids. Here we determine the role of BASP1 lipidation in histone modification and the modulation of chromatin accessibility. We find that the removal of the active histone modifications H3K9ac and H3K4me3 by BASP1 requires the N-terminal myristoylation of BASP1. In contrast, the placement of the repressive histone modification, H3K27me3, by BASP1 does not require BASP1 lipidation. RNA-seq and ATAC-seq analysis finds that BASP1 regulates the activity of multiple transcription factors and induces extensive changes in chromatin accessibility. We find that ∼50% of BASP1 target genes show lipidation-dependent chromatin compaction and transcriptional repression. Our results suggest that BASP1 elicits both lipid-dependent and lipid-independent functions in histone modification and transcriptional repression. In accordance with this, we find that the tumor suppressor activity of BASP1 is also partially dependent on its myristoylation.

INTRODUCTION BASP1 was initially identified as a cytoplasmic signaling protein in neuronal cells (Mosevitsky, 2005) but has since been found to be widely expressed and localized to the nucleus in several cell types via a canonical nuclear localization sequence (Carpenter et al., 2004;Goodfellow et al., 2011;. A role for BASP1 in transcriptional regulation was first identified as a cofactor for the Wilms' tumor one protein WT1 (Carpenter et al., 2004). BASP1 binds to WT1 and converts it from a transcriptional activator to a repressor. BASP1 has subsequently been found to act as a repressive cofactor for other transcriptional regulators including MYC (Hartl et al., 2009), ERa (Marsh et al., 2017), and YY1 (Santiago et al., 2021), suggesting that it is broadly deployed in transcription control (Hartl and Schneider, 2019). Our understanding of how BASP1 regulates transcription is limited and has, to date, largely been studied as a transcriptional corepressor of WT1. BASP1 is N-terminally myristoylated and we demonstrated that this lipid motif is required for its function as a transcriptional repressor (Toska et al., 2012. BASP1 also contains a consensus cholesterol binding motif adjacent to the myristoyl moiety (Epand, 2008). Indeed, both phosphatidylinositol 4,5-bisphosphate (PIP 2 ) and cholesterol are recruited to promoter regions and are required for transcriptional repression by BASP1 (Toska et al., 2012Loats et al., 2021). The myristoylation of BASP1 is required for interaction with both PIP 2 and cholesterol (Toska et al., 2012;Loats et al., 2021). PIP 2 assists in the recruitment of HDAC1 to mediate the deacetylation of histone H3K9 and direct transcriptional repression (Toska et al., 2012).
Several studies have demonstrated that BASP1 acts as a tumor suppressor, for example in hepatocellular carcinoma (Tsunedomi et al., 2010), gastric cancer (Li et al., 2020), and breast cancer (Marsh et al., 2017). Consistent with its tumor suppressor activity, BASP1 has a role in maintaining the differentiated state and is required to maintain the functional differentiated state of taste receptor cells in mice (Gao et al., 2014(Gao et al., , 2019. BASP1 can also block the transformation of fibroblasts by v-myc (Hartl et al., 2009). Moreover, BASP1 interferes with the action of the Yamanaka proteins in the induction of pluripotent stem cells by repressing WT1 target genes (Blanchard et al., 2017). Taken together, current studies suggest that BASP1 plays a major role in the maintenance of the differentiated state through the modulation of the function of several transcriptional activator proteins.
The mechanism of action of BASP1 as a transcription cofactor is poorly understood, but its physiological activities suggest extensive roles in gene regulation. In this study, we set out to determine how BASP1 regulates transcription through histone modifications and the roles of the myristoyl motif of BASP1. We find that the lipidation of BASP1 plays a selective role in its action as a transcription cofactor and is required for specific histone modification leading to the modulation of the chromatin environment in order to elicit specific transcriptional effects. Our results suggest that BASP1 plays a widespread role in regulating the chromatin environment and transcription control.

RESULTS
BASP1 mediates the removal of active histone modifications and the placement of repressive H3K27me3 K562 cells express endogenous WT1 but do not express BASP1 (Goodfellow et al., 2011). As we have shown before, the introduction of BASP1 into K562 cells (B-K562 cells) leads to the transcriptional repression of the WT1 target genes AREG and VDR compared to the control K562 cells (V-K562 cells; Figure 1A; Goodfellow et al., 2011;Toska et al., 2012Loats et al., 2021). We next performed chromatin immunoprecipitation (ChIP) to detect WT1, BASP1, and a selection of specific histone modifications at the promoter regions of the WT1 target genes AREG and VDR ( Figure 1B). WT1 was present at the promoter region of the AREG and VDR genes in both control V-K562 cells and B-K562 cells, while BASP1 was present at the promoter region only in B-K562 cells. Analysis of H3K9ac and H3K4me3, indicators of transcriptionally active promoter regions, demonstrated that these histone modifications are removed when BASP1 is recruited to the promoter in B-K562 cells and the genes are transcriptionally repressed. These data are consistent with our previous studies of BASP1 function in histone modification (Toska et al., 2012;Loats et al., 2021). We also analyzed the H3K27me3 histone modification associated with transcriptional repression. We found that BASP1 induces the trimethylation of H3K27 at both the AREG and VDR promoter regions. Taken together, these results demonstrate that BASP1 functions by both the removal of active histone modifications (H3K9ac and H3K4me3) and the placement of the repressive histone mark H3K27me3.
We have previously used a conditional BASP1 mouse to demonstrate that BASP1 transcriptionally represses the WT1 target genes LEF1 and PTCH1 in taste receptor cells (Gao et al., 2014(Gao et al., , 2019. A floxed BASP1 mouse was crossed with a Krt8-Cre-ER mouse to delete BASP1 in the fully differentiated Krt8-expressing taste cells. We used this model to perform ChIP to analyze BASP1-dependent histone modifications at the LEF1 and PTCH1 promoters in taste receptor cells. Consistent with our previous studies, knockout of BASP1 expression in taste cells leads to the upregulation of LEF1 and PTCH1 expression ( Figure 1C). ChIP analysis confirmed that WT1 was present in the promoter region of the LEF1 and PTCH1 genes in both the control and BASP1 KO taste cells while BASP1 was only present in the promoter regions in the taste receptor cells from control mice ( Figure 1D). Analysis of H3K9ac, H3K4me3, and H3K27me3 demonstrated that loss of BASP1 leads to the accumulation of the active histone modifications H3K9ac and H3K4me3, and the removal of the H3K27me3 repressive modification. These findings, using an in-vivo model of BASP1 function, confirm and extend our analysis in K562 cells demonstrating that BASP1 directs the removal of the active histone modifications H3K9ac and H3K4me3 and the placement of repressive H3K27me3.

Myristoylated BASP1 is recruited to gene promoters
Our previous work demonstrated that the mutation BASP1-G2A, which prevents N-terminal myristoylation of BASP1, leads to loss of transcriptional repression function (Toska et al., 2012. Although total cellular BASP1 is stoichiometrically N-terminally myristoylated (Mosevitsky, 2005), it has not yet been demonstrated that this lipidated form of BASP1 is present within the nucleus and is directly recruited to gene promoter regions. We, therefore, used a click chemistry approach to analyze the myristoylation status of nuclear BASP1 in K562 cell line derivatives. V-K562 and B-K562 cells along with G-K562 cells (expressing BASP1-G2A) were incubated in lipid-free media supplemented with myristic acid alkyne that is utilized by cells as a substrate for N-terminal myristoylation (Wright et al., 2015). Nuclei were then prepared as we described before using a method that avoids contamination with either ER or Golgi complex (Loats et al., 2021). Using click chemistry, the alkyne-myristoyl moiety was crosslinked to azide-PEG3-biotin which was then detected using a streptavidin-linked fluorophore. BASP1 was simultaneously probed using immunocytochemistry. In control V-K562 cells, myristoyl was detected within the purified nuclei of K562 cells and was particularly evident at the nuclear membrane ( Figure 2A). This staining pattern is consistent with the known N-terminal myristoylation of lamins (Linde and Stick, 2010 iScience Article colocalization with myristoyl compared to wtBASP1 (Pearson quantitation of the proportion of BASP1 signal that overlaps with myristoyl is shown in Figure 2B).
We next used click chemistry combined with immunoprecipitation to determine the association between BASP1 and nuclear myristoyl. V-K562, B-K562, and G-K562 cells were cultured in the presence of myristic acid alkyne as above for 48 h and, following the click chemistry reaction with azide-PEG3-biotin, nuclear protein extracts were prepared then immunoprecipitation was performed with streptavidin-coated beads. wtBASP1, but not BASP1-G2A, co-immunoprecipitated with the alkyne-myristoyl ( Figure 2C). We have previously combined click chemistry with ChIP (Click-ChIP; Loats et al., 2021) and therefore used this technique A B C D Figure 1. BASP1 directs the removal of active histone modifications H3K9ac and H3K4me3 and the placement of repressive H3K27 trimethylation (A) Immunoblotting of extracts prepared from V-K562 and B-K562 cells to confirm BASP1 expression. b-tubulin immunoblotting was performed as a loading control. Molecular weight markers are shown at left (kDa). cDNA was prepared from V-K562 cells and B-K562 cells and expression of AREG and VDR was quantitated relative to GAPDH. Data are SD from the mean (SDM) for three independent experiments. * = p < 0.05 by students t-test. (B) V-K562 or B-K562 cells were subjected to chromatin immunoprecipitation (ChIP) with control (ctrl) antibodies or antibodies against WT1, BASP1 or the histone modifications indicated. Data are presented as fold-enrichment over a control genomic region and error bars are SDM of three independent experiments. * = p < 0.05 by students t-test.
(C) Krt8-BASP1-CRE mice were treated with tamoxifen for 8 days and then 7 days later taste buds were isolated and RNA prepared. cDNA was then used to monitor the expression of BASP1, LEF1, and PTCH1 compared to GAPDH. Error bars are SDM for three independent experiments. * = p < 0.05 by students t-test.
(D) Mice were treated as in part C but isolated taste cells were subjected to chromatin immunoprecipitation with the antibodies indicated. Error bars are SDM for three independent experiments. * = p < 0.05, ** = p < 0.01 and *** = p < 0.005 by students t-test.

OPEN ACCESS
iScience 25, 104796, August 19, 2022 3 iScience Article to determine if myristoyl is recruited to the promoter region of WT1 target genes. We found that myristoyl associates with the AREG and VDR promoters at a significantly increased level when wtBASP1 is also present at the promoter ( Figure 2D; compare V-K562 with B-K562), but at a reduced level in K562 cells that express BASP1-G2A (G-K562). Our previous study (confirmed in Figure 3B) demonstrated that BASP1 G2A is recruited to gene promoters at a level equivalent to wtBASP1 (Toska et al., 2012. These data demonstrate that the detection of myristoyl in the promoter region of WT1 target genes is dependent on BASP1 and that this association requires an intact G2 target site of myristoylation in BASP1.
Myristoylation of BASP1 is required for the removal of active chromatin modifications but not for the placement of repressive H3K27me3 Our data so far have demonstrated that myristoylated BASP1 is present at the promoter region of WT1 target genes. Our previous work (Toska et al., 2012, confirmed in Figures 3A and 3B, has shown A B C D Figure 2. Myristoylated BASP1 is present in the nucleus and is recruited to the promoter regions of WT1 target genes (A) The indicated cell lines were incubated in lipid-free media in the presence of 10 mg/mL myristic acid alkyne for 20 h. Nuclei were prepared and incubated with azide PEG3-biotin, then the click reaction was initiated using 2mM CuBF 4 . Immunohistochemistry was then performed with streptavidin-linked antibodies (Myristoyl) and BASP1 antibodies. Scale bar (blue) is 10mm. Control assays were performed that lacked azide PEG3-biotin (-azide control) or myristic acid alkyne (-alykyne control) and are shown at right. (B) Quantification of the colocalization of BASP1 and Myristoyl in B-K562 and G-K562 nuclei was analyzed using Pearson's correlation coefficient. *** = p < 0.005 following students t-test comparing Pearson's values from BK and GK nuclei over three independent experiments (n = 67).
(C) V-K562, B-K562, and G-K562 cells were incubated with ethanol (-alkyne control) or 10 mg/mL alkyne-Myristic acid as above and subjected to a click chemistry reaction. Nuclear extracts were prepared and precipitation was performed with streptavidin beads. Immunoblotting was then performed with either BASP1  iScience Article that BASP1-G2A lacks transcriptional corepressor activity despite being recruited along with WT1 to the promoter region of WT1 target genes. We demonstrated before that BASP1-G2A is defective in the deacetylation of H3K9 (Toska et al., 2012). We, therefore, tested if BASP1-G2A was able to mediate the demethylation of H3K4me3 or the placement of repressive H3K27me3. ChIP analysis confirmed that BASP1-G2A was defective in the removal of the active H3K9ac mark ( Figure 3C). We also found that BASP1-G2A was defective in the removal of H3K4me3. Thus, myristoylation of BASP1 is required for the removal of active histone modifications. In contrast, BASP1-G2A was still able to support the placement of repressive H3K27me3 at both the AREG and VDR promoters. Thus, the N-terminal myristoylation of BASP1 is required to mediate the removal of the active histone modifications, but is not required to place the repressive H3K27me3 modification. These findings suggest a dual mechanism of lipidation-dependent and lipidation-independent transcriptional repression by BASP1.

Myristoylation of BASP1 plays a role in the tumor suppressor activity of BASP1
BASP1 acts as a tumor suppressor in several cell types and slows the growth of K562 cells (Goodfellow et al., 2011;. We, therefore, tested the effects of wtBASP1 and BASP1-G2A on the formation of anchorage-independent colonies of K562 cells in soft agar assays ( Figure 3D). The number of V-K562, B-K562, and G-K562 colonies greater than 50 cells in size were counted over a three-week period ( Figure 3E). Expression of wtBASP1 led to a significant decrease in the colony-forming ability of K562 cells. Measurement of the average area of colonies revealed that, as well as forming fewer colonies, B-K562 cells form significantly smaller colonies ( Figure 3F). Expression of BASP1-G2A also led to a significant decrease in the colony-forming ability of K562 cells ( Figure 3E). However, the average area of G-K562 colonies did not significantly differ from that of V-K562 colonies ( Figure 3F). Thus, myristoylation of BASP1 is not required to suppress the formation of anchorage-independent colonies but contributes to inhibiting the growth of the established colonies. We conclude that the myristoylation of BASP1 plays a partial role in its tumor suppressor function in K562 cells.

Genome-wide analysis of the requirement for BASP1 myristoylation in transcriptional repression
Our results so far demonstrate that BASP1 directs histone modifications that are both dependent (H3K9 deacetylation and H3K4 demethylation) and independent (H3K27 methylation) on the N-terminal myristoylation of BASP1. In addition, myristoylation of BASP1 is partially required for its tumor suppressor activity. Taken together, these findings suggest that non-myristoylated BASP1 is likely to retain partial activity in transcriptional regulation that we have not observed here or before (Toska et al., 2012 in targeted gene analysis. We, therefore, determined the gene expression profiles of B-K562, G-K562, and control V-K562 cells using RNAseq. Comparing the data from V-K562 and B-K562 cells ( Figure 4A) supports our previous genome-wide analysis with 3064 genes regulated by BASP1 (2165 genes repressed and 899 genes activated; Table S1; Goodfellow et al., 2011). As before, the majority of the known WT1 target genes that show altered expression were transcriptionally repressed (70.3% of the 105 WT1 target genes that changed significantly (padj 0.05); Goodfellow et al., 2011;Figures 4B and 4C). Our data further show that BASP1-G2A is partially defective in transcriptional repression when compared to wtBASP1, repressing 58.8% of the genes that are repressed by wtBASP1 ( Figure 4A). This was reduced to 45.7% when considering only WT1 target genes ( Figures 4B and 4D). In fact, BASP1-G2A activated many of the target genes that are repressed by wtBASP1 ( Figure 4D).
Pathway enrichment analysis of genes repressed by wtBASP1 reveals several networks (canonical pathways and upstream regulators) that are enriched for significantly repressed differentially expressed genes in  iScience Article B-K562 cells, and further demonstrate their derepression in G-K562 cells (Figures 4E and 4F). We also identified other networks downregulated in B-K562, but not G-K562 cells, including those involved in the response to the H3K27 Demethylase Inhibitor GSK-J4, the selective estrogen receptor modulator Tamoxifen, the extra-cellular space, and pre-ribosome cellular processes ( Figure S1A). Together these data show the repressive effects of wtBASP1 are partially lost with the BASP1-G2A mutant derivative, specifically, the BASP1-dependent downregulation of transcription factors, upstream regulators, drug responses, and canonical pathways genome-wide ( Figures 4E, 4F, and S1A). BASP1 was also found to regulate the function of a number of transcription factors, in particular MYC, RELA, TFCP2, POU3F2, YY1, and CTCF ( Figure S1B; note that the expression level of the transcription factors was unchanged by wtBASP1 or BASP1 G2A compared to control cells). In the case of MYC, the effect of wtBASP1 was both activation and repression and a similar profile for MYC target genes was observed with K562 cells expressing BASP1-G2A. In contrast, wtBASP1 was predominantly repressive at RELA, TFCPP2, POU3F2, YY1, and CTCF target genes but BASP1-G2A elicited transcriptional activation at most genes that changed expression. Taken together the data in Figure 4 demonstrate that the myristoylation of BASP1 is required for its transcriptional repressor function at $50% of target genes. Furthermore, BASP1 regulates the expression of the target genes of both previously described BASP1-associated transcription factors (MYC, YY1, CTCF) and newly identified targets.

The effects of BASP1 and requirement for myristoylation on the chromatin landscape
Our targeted ChIP analysis of histone modifications (Figures 1 and 3) suggests that BASP1 likely makes extensive changes in the chromatin environment. Furthermore, the BASP1-G2A mutant derivative is defective in the removal of active chromatin modifications (H3K9ac and H3K4me3) but not the addition of repressive chromatin modification (H3K27me3). To investigate the effects of these BASP1-dependent chromatin remodeling events genome-wide, we conducted sequencing experiments to Assay for Transposase-Accessible Chromatin (ATAC-seq; Table S1). Comparing control V-K562 cells with B-K562 cells the data demonstrate that chromatin accessibility is substantially altered genome-wide by wtBASP1 with 79% of the peak changes in closed chromatin compared to 21% of the changes in open chromatin ( Figure 5A). These data are consistent with broad transcriptional repression by BASP1. 15% of the peak changes were within the upstream regions and transcription start sites of genes, but there were also general BASP1-dependent changes across the gene bodies. When we compared the ATAC-seq data with the RNA-seq data, we observed a strong correlation between the BASP1-dependent formation of closed chromatin within the gene proximity and transcriptional repression of the associated gene by BASP1 ( Figure 5B). Specifically, 514 genes that are transcriptionally repressed by BASP1 showed BASP1-dependent formation of closed chromatin peaks. Analysis of these genes by g:profiler highlighted several categories related to the physiological functions of BASP1 including developmental processes and cell differentiation ( Figure S2A). BASP1-G2A caused more peak changes in ATAC-Seq than wtBASP1 ( Figure S2B), although the ratio of changes in open chromatin versus changes in closed chromatin was increased ( Figure 4C). Direct comparison of wtBASP1 with BASP1-G2A showed that 66.3% of genes affected by wtBASP1 correlated with both decreased chromatin accessibility and increased transcriptional repression, while for BASP1-G2A, this only occurred for 45.5% of the genes ( Figure 4D). Indeed, BASP1-G2A was either completely ( Figure 4E, cluster 3) or partially ( Figure 4E cluster 4) defective in inducing closed regions of chromatin at most genomic regions modified by wtBASP1 (Non-relative data are shown in Figure S2C). Thus, although BASP1-G2A showed a defect in driving closed chromatin at many of the sites acted upon by wtBASP1, it did not show an overall lower activity in driving the formation of closed chromatin. This is consistent with BASP1-G2A acting at several sites that are not regulated by wtBASP1. Figure S3A shows a pathway analysis demonstrating that wtBASP1 and BASP1 G2A regulate many genes involved in distinct cellular processes. iScience Article Figure 5F shows the comparison of RNA-seq and ATAC-seq analysis across four WT1 target genes, AREG, VDR, EREG, and VEGFA. All four loci show a reduction of RNA-seq signal across the genes in B-K562 cells compared to V-K562 cells, which is coincident with a decrease in the chromatin accessibility around the transcription start sites of each gene (as measured by ATAC-Seq; Figure S3B shows an expanded view with surrounding genomic regions). In G-K562 cells, AREG, VDR, and EREG are not transcriptionally repressed (in-fact, BASP1-G2A activates the transcription of EREG), while VEGFA is still partially repressed by BASP1-G2A. Both EREG and AREG show that BASP1-G2A fails to induce closed ATAC peaks in the vicinity of the transcription start site which is coincident with the failure of BASP1-G2A to repress the transcription of these genes. BASP1-G2A produced an ATAC profile similar to wtBASP1 at the transcription start site region of the VDR gene but instead showed a reduction in ATAC peaks within the main body of the gene. Taken together, these data suggest that BASP1 transcriptionally represses WT1 target genes through the formation of closed chromatin. Furthermore, BASP1-G2A shows gene-specific effects in function, both in the regulation of chromatin compaction and in transcriptional repression.

DISCUSSION
BASP1 represses transcription through a novel mechanism that requires its lipidation to recruit PIP 2 and cholesterol to the promoter region of target genes. The localization of lipids, including PIP 2 and cholesterol, within the nucleus as well as their association with chromatin is well-documented, but their roles in nuclear processes are poorly understood (Garcia-Gil and Albi, 2017; Fernandes et al., 2018;Fiume et al., 2019;Barbosa and Siniossoglou, 2020;Gapa et al., 2022). Here we have presented evidence that myristoylated BASP1 is recruited to the promoter of WT1 target genes. We find that the N-terminal myristoylation of BASP1 controls its ability to modulate the chromatin environment and is needed for the full tumor suppressor activity of BASP1.
We have previously shown that BASP1interacts with PIP 2 to recruit HDAC1 to the gene promoter, which then deacetylates histone H3K9. The enzymes responsible for the BASP1-dependent demethylation of K3K4 and the trimethylation methylation of H3K27 have yet to be identified. It will be interesting to determine if BASP1 recruits the polycomb complex to trimethylate H3K27. BASP1-G2A retains the function to trimethylate H3K27 but is not able to mediate the deacetylation of H3K9 or demethylation of H3K4. This raises the possibility that BASP1-G2A leads to the formation of bivalent histone marks. Bivalent marks are associated with poised promoter regions that can either be activated or repressed (Voigt et al., 2013). The BASP1-G2A mutant derivative still causes significant changes in chromatin accessibility, suggesting that the H3K27 trimethylation function is sufficient, at least for a subset of target genes, to remodel chromatin and alter gene expression. Indeed, our RNAseq data demonstrate that BASP1-G2A retains transcriptional repressor activity at $46% of WT1 target genes and $59% overall. Genome-wide analysis of BASP1-and myristoylation-dependent histone modifications could be used to determine if BASP1-G2A leads to the formation of bivalent chromatin. Several previous studies have demonstrated the tumor suppressor activity of BASP1 (Hartl and Schneider, 2019). We found that BASP1-G2A is partially defective in tumor suppressor function which is consistent with the retention of chromatin modification and transcriptional regulatory function. It will be interesting to determine if BASP1 myristoylation plays a similar role in its tumor suppressor activity in other cell types.
The BASP1 G2A mutation prevents terminal myristoylation of BASP1 and abolishes its interaction with lipids. In that respect, the findings presented here raise the question of whether the lipid-binding function of wtBASP1 can be modulated to provide a regulatory mechanism for the transcriptional repression function of BASP1. Indeed, previous studies have demonstrated that the phosphorylation of Serine-6 within iScience Article BASP1 generates an unfavorable environment for the binding of lipids by BASP1 (Mosevitsky, 2005). We have previously reported that the phosphorylation of BASP1 disrupts its transcriptional repressor activity (Toska et al., 2012). This raises the possibility that the phosphorylation of BASP1 might regulate its ability to interact with different chromatin remodeling activities in the regulation of the chromatin landscape.
Our RNAseq data suggest that BASP1 can regulate the activity of several transcription factors. These include transcription factors previously identified as partners of BASP1, MYC (Hartl et al., 2009), YY1 (Santiago et al., 2021, and CTCF (Essafi et al., 2011). Several target genes of MYC and the associated MAZ protein were regulated by BASP1 which had both coactivator and corepressor function that was not dependent on the myristoylation of BASP1. This reduced dependence on BASP1 myristoylation might be owing to the proposed alternative mechanism whereby BASP1 interferes with the MYC-Calmodulin interaction. In contrast, YY1 and CTCF target genes showed BASP1-dependent repression that was substantially dependent on BASP1 myristoylation. RELA and TFAP2A/C target genes were also repressed by BASP1 which was dependent on BASP1 myristoylation. Taken together, these results suggest that BASP1 modulates the transcriptional activity of several transcription factors through both lipidation-dependent and lipidation-independent mechanisms.

Limitations of the study
In this study, we evaluated the effect of the disruption of the myristoyl motif of BASP1 on its function as a transcriptional coregulator. As the G2A mutation in BASP1 disrupts the interactions with both PIP 2 and cholesterol, the current study does not discriminate between the contribution of these two lipids to transcriptional regulation. Although we demonstrated that the BASP1-dependent effects on H3K9, H3K4, and H3K237 modification status occur in both K562 cells and in murine taste cells, the genome-wide study was limited to K562 cells. RNAseq and ATACseq in other cell types will be required to confirm the generality of these effects. Further studies to determine the genome-wide localization of BASP1 will be required to distinguish between direct and indirect effects of BASP1 or BASP1 G2A in the RNAseq and ATACseq datasets. Finally, the gene distance cut-off of 10kb that was applied to the analysis of potential regulatory elements is an accepted standard for such analyses, providing a suitable balance of sensitivity and specificity for the detection of cis-acting regulatory effects. However, we recognize that this approach excludes distant regulatory elements.

STAR+METHODS
Detailed methods are provided in the online version of this paper and include the following:

Materials availability
This study did not generate unique reagents.

Data and code availability
All data reported in this paper will be shared by the lead contact upon request. The RNA-seq and ATACseq data are available at the NCBI Gene Expression Omnibus GSE196525. This paper does not report original code. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
Stably transfected K562 cell line derivatives selected as a pool (Goodfellow et al., 2011;Toska et al., 2012) were supplemented with 1 mg/mL G-418 (Sigma-Aldrich). All cell line derivatives were screened by immunofluorescence to ensure that BASP1 was expressed in >95% of the cells. Cells were incubated at 37 C in humidified 95% air and 5% CO 2 . Lipid-free media was prepared as above but using charcoalstripped fetal calf serum (Thermofisher).

Chromatin immunoprecipitation
K562 cells were harvested and resuspended to a density of 1 3 10 6 cells/mL in PBS. Crosslinking was initiated by the addition of formaldehyde to a final concentration of 1.42% (v/v) and incubation at room temperature for 15 min. Crosslinking was terminated by the addition of glycine to a final concentration of 125mM glycine and incubation at 4 C for 5 min. The cells were then harvested by centrifugation at 2000 x g for 5 min at 4 C and then washed with PBS. Cells were resuspended and lysed in 1mL IP buffer (150mM NaCl, 50mM Tris-HCl (pH 7.5), 5mM EDTA, 0.5% (v/v) NP-40 and 1% (v/v) Triton X-100) with protease inhibitor cocktail for 15 min on ice. After centrifugation at 2000 x g for 5 min at 4 C, the pellet resuspended in 1mL IP buffer. The chromatin was sheared by sonication using a QSonica Q500 at 60% amplitude and the lysate was cleared by centrifugation at 12000 x g for 10 min at 4 C. The samples were pre-cleared by iScience 25, 104796, August 19, 2022 iScience Article incubation for 1 h at 4 C on a rotator with 10mL Protein G magnetic beads (ThermoFisher). The indicated antibody was incubated in 600mL IP buffer containing 1mL 10 mg/mL acetylated BSA and 5mL Protein G magnetic beads for 4 h with rotation at 4 C. 200mL of the pre-cleared chromatin was added and incubation continued at 4 C overnight. A sample of the input chromatin was stored for later decrosslinking and processing.

qPCR analysis
Total RNA was prepared using the RNeasy kit (Qiagen) and then cDNA prepared using the Iscript cDNA synthesis kit (Bio-Rad). qPCR was performed in triplicate using the iQ SYBR green master mix (Bio-Rad) on a Bio-Rad Miniopticon system. ChIP analysis was performed using promoter-specific primers as indicated and fold-enrichment calculated against a control genomic region. Melt curve analysis was performed at the end of each run. Data were collected using the BioRad-CFX Manager software. The relative expression of each target gene is expressed relative to control GAPDH. Data are expressed as mean with SD(SDM). Data distribution and significance between different groups was analyzed in Excel or OriginPro 7.5 using Student's t test.
Immunofluorescence K562 cell line derivatives were collected by centrifugation at 1400 x g for 3 min and nuclei were isolated using the Nuclei EZ Prep nuclei isolation kit (Sigma-Aldrich). The nuclei were fixed in 4% (v/v) paraformaldehyde at room temperature for 15 min, then incubated with 50mM NH 4 Cl for 15 min with rotation. The fixed nuclei were then washed three times in PBS and then incubated in blocking buffer (2% (w/v) BSA, 0.25% (w/v) Gelatin, 0.2% (w/v) Glycine, 0.2% (v/v) Triton X-100 in PBS) for 1 h with rotation at room temperature. Primary antibody was added to each sample of nuclei in PBS containing 1% (w/v) BSA, 0.25% (w/v) gelatin and 0.2% (v/v) Triton X-100 and then incubated for 1 h with rotation at room temperature. The nuclei were then washed three times with washing buffer (0.2% (w/v) Gelatin in PBS). Fluorescent secondary antibody in PBS containing 1% (w/v) BSA, 0.25% (w/v) gelatin and 0.2% (v/v) Triton X-100 was then incubated with the nuclei for 45 min in with rotation in the dark. After washing three times the samples were incubated with DAP solution for 10 min and then resuspended in a minimum volume of DABCO mounting media (Sigma-Aldrich) an applied to poly-lysine coated slides. Nuclei were viewed using a Leica SP5-II AOBS confocal laser scanning microscope attached to a Leica DM I6000 inverted epifluorescence microscope with oil 63x lens. Images were processed using ImageJ or Volocity 6.3 software.

Click chemistry
K-562 cells were incubated in lipid free media containing 10 mg/mL myristic acid-alkyne (Avanti) for 16 h. Cells were then harvested and washed once in PBS and the nuclei isolated using the EZ Prep nuclei kit. The purified nuclei were then incubated with 50mM Azide-PEG3-biotin conjugate (Sigma-Aldrich) in Buffer A and added to samples. The click reaction was initiated via addition of 2mM CuBF4 in 2% (v/v) acetonitrile, and the reaction was left to proceed at 43 C for 30 min with rotation. The nuclei were then washed in 0.1M HEPES/KOH (pH 7.4) and immediately used for either preparation of nuclear extract for immunoprecipitation, chromatin immunoprecipitation or immunofluorescence.

Immunoprecipitation
Following completion of the click chemistry reaction, nuclear extracts were prepared by resuspending samples in one PCV of NE2 buffer (20 mM HEPES pH 8.0, 1.5 mM MgCl2, 25% (v/v) Glycerol, 420 mM NaCl, 0.2 mM EDTA, 1 mM DTT and 0.5 mM PMSF) and incubating on ice with regular stirring for 30 min. Nuclear debris was pelleted by 5 min in a microfuge at full speed. The cleared nuclear extracts were then subjected to immunoprecipitation using streptavidin magnetic beads (Invitrogen) and IP buffer (

Colony formation assay
The K562 cell line derivatives were seeded onto agar bases (0.7% (w/v) LMP Agarose, 10% (v/v) FBS, 1% (v/v) Pen Strep, 1% (v/v) L-glutamine in DMEM media supplemented with 0.15% (v/v) NaHCO3, 200 mM sodium pyruvate and 800 mM NaOH at a density of 2 3 105 cells per mL of agar. Agar dishes were fed with additional agar (2 mL) on days 7 and 14 post seeding. The colony formation efficiency (%CFE) was calculated 7, 14 and 21 days post seeding via counting colonies consisting of greater than 50 cells, in a minimum of ten 15.89 mm 2 fields of view per 60 mm 2 dish. Average colony area was measured using ImageJ software. All experiments were performed in triplicate.

Animals
The floxed BASP1 mice in a C57BL/6 background have been described before (Gao et al., 2019). We have not detected any sex differences related to the effects of BASP1 loss. However, we routinely kept records of both sex and age (2-5 months) to ensure no bias is introduced. Animals were cared for in compliance with the University at Buffalo Animal Care and Use Committee. BASP1fl+/À mice were mated with Krt8-Cre/ ERT2+ mice (The Jackson Laboratory) to obtain BASP1fl+/ÀKrt8-Cre/ERT2+ mice and then bred to obtain BASP1fl+/+Krt8-Cre/ERT2+ mice. BASP1flÀ/À; Krt8-Cre/ERT2+ or BASP1fl+/+; Krt8-Cre/ERT2À littermates were used as wild-type controls. Adult mice were gavaged with 100 mg/kg body weight of Tamoxifen dissolved in corn oil/ethanol (9:1 by volume) daily for 8 days 7 days later the mice were euthanized and taste receptor cells harvested from the circumvallate papillae as described before (Dutta Banik et al., 2018). The mice were euthanized by carbon dioxide followed by cervical dislocation and the tongues removed. A solution containing 0.7 mg/mL Collagenase B (Roche), 3 mg/mL Dispase II (Roche), and 1 mg/mL trypsin inhibitor (Sigma-Aldrich) was then injected beneath the lingual epithelium. The tongues were then incubated in in oxygenated Tyrode's solution for 15 min and then the epithelium peeled from the muscle and placed in Ca2+-free Tyrode's. The cells were used immediately for either RNA preparation or ChIP.

Sample preparation and sequencing
All samples were prepared in biological triplicates for V-K562, B-K562 and G-K562 cells harvested at 0.7-0.8 3 10 6 cells/mL (Loats et al., 2021;Toska et al., 2012). Cells taken for ATAC-seq were treated with DNase and incubated for 30 mins buffered with 25mM MgCl2 and 5mM CaCl2 prior to harvesting. Samples of 2 3 10 6 for RNA-seq and of 1 3 10 5 cells for ATAC-seq were then pelleted and snap-frozen in liquid nitrogen until library preparation. Total RNA was isolated from the cells using the RNeasy Mini Kit (Qiagen, cat 74104), RNA Integrity Number (RIN) was determined for each sample using Tape Station 4150 System (Agilent). Libraries were prepared according to manufacturer's instructions for TruSeq Stranded mRNA Library kit (Illumina, cat #20020594) and ATAC-Seq Kit (Active Motif, cat #53150). Libraries were sequenced on Illumina NextSeq 500 in 75-nt (RNA-seq) and 42-nt (ATAC-seq) experiments in paired-end mode.

RNA-seq and ATAC-seq data analysis
Reads were mapped to GRCh38 human reference genome using STAR (Dobin et al., 2013) (RNA-seq) and BWA-MEM (Li and Durbin, 2009) (ATAC-seq), duplicate reads were removed, uniquely mapping reads with %2 mismatches were kept for downstream analysis. RNA-seq paired reads were assigned to RefSeq transcripts using Subread and annotated with Entrez gene identifiers. ATAC peaks were called using MACS2, merged regions were defined by overlapping intervals between all samples, peak fragment densities for merged regions were used for comparative analysis and annotated with the closest gene within a 10kb window. DESeq2 was used for differential expression analysis to determine p-adjusted values (padj) and shrunken Log2 fold-change (FC) for RNA-seq genes and ATAC-seq merged regions. (Gaspar, 2018;Liao et al., 2013;Love et al., 2014) The 5000 most differentially expressed genes for BK vs VK at padj %0.05 were analyzed using iPathway to determine significantly differentially regulated networks. The 'Impact Analysis' was applied to underlying pathway topologies comprised of genes and their directional interactions. The probability of observing the number of DE genes in a given pathway that is R to that observed by random chance (pORA) and perturbation accumulation, based on the combined perturbation of all genes within the pathway as a function of normalised change in expression and weighting in relation to gene type, (pAcc), are the principal measures ll OPEN ACCESS iScience Article